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Researcher
- Peeyush Nandwana
- Rama K Vasudevan
- Sergei V Kalinin
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- Maxim A Ziatdinov
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- Gordon Robertson
- Gs Jung
- Gyoung Gug Jang
- Hoyeon Jeon
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- Mahim Mathur
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- Omer Onar
- Ondrej Dyck
- Oscar Martinez
- Ozgur Alaca
- Peter L Fuhr
- Peter Wang
- Radu Custelcean
- Ryan Dehoff
- Saban Hus
- Sai Mani Prudhvi Valleti
- Sam Hollifield
- Steven J Zinkle
- Steven Randolph
- Sumner Harris
- Tim Graening Seibert
- Tomas Grejtak
- Utkarsh Pratiush
- Weicheng Zhong
- Wei Tang
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Dual-GP addresses limitations in traditional GPBO-driven autonomous experimentation by incorporating an additional surrogate observer and allowing human oversight, this technique improves optimization efficiency via data quality assessment and adaptability to unanticipated exp

This technology can help to increase number of application areas of Wireless Power Transfer systems. It can be applied to consumer electronics, defense industry, automotive industry etc.

The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

The invention introduces a novel, customizable method to create, manipulate, and erase polar topological structures in ferroelectric materials using atomic force microscopy.

Faults in the power grid cause many problems that can result in catastrophic failures. Real-time fault detection in the power grid system is crucial to sustain the power systems' reliability, stability, and quality.

High coercive fields prevalent in wurtzite ferroelectrics present a significant challenge, as they hinder efficient polarization switching, which is essential for microelectronic applications.

Scanning transmission electron microscopes are useful for a variety of applications. Atomic defects in materials are critical for areas such as quantum photonics, magnetic storage, and catalysis.

Distortion in scanning tunneling microscope (STM) images is an unavoidable problem. This technology is an algorithm to identify and correct distorted wavefronts in atomic resolution STM images.

A human-in-the-loop machine learning (hML) technology potentially enhances experimental workflows by integrating human expertise with AI automation.